Improving Wealth Management Strategies Through the Use of Reinforcement Learning Based Algorithms. A Study on the Romanian Stock Market

Ștefan-Constantin RADU, Lucian ANGHEL, Ioana Simona ERMIȘ


In the context of the growing pace of technological development and that of the transition to the knowledge-based economy, wealth management strategies have become subject to the application of new ideas. One of the fields of research that are increasing in influence in the scientific community is that of reinforcement learning-based algorithms. This trend is also manifesting in the domain of economics, where the algorithms have found a use in the field of stock trading. The use of algorithms has been tested by researchers in the last decade due to the fact that by applying these new concepts, fund managers could obtain an advantage when compared to using classic management techniques. The present paper will test the effects of applying these algorithms on the Romanian market, taking into account that it is a relatively new market, and compare it to the results obtained by applying classic optimization techniques based on passive wealth management concepts. We chose the Romanian stock market due to its recent evolution regarding the FTSE Russell ratings and the fact that the country is becoming an Eastern European hub of development in the IT sector, these facts could indicate that the Romanian stock market will become even more significant in the future at a local and maybe even at a regional level.

Full Text:



Brock, W., Lakonishok, J., & Lebaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47, 1731–1764.

Cajas D. (n.d.). Riskfolio library. Retrieved from

Carr M. (2021). Turtle Trading: A Market Legend. Retrieved from

Dempster, M. A. H., & Leemans, V. (2006). An automated FX trading system using adaptive reinforcement learning. Expert Systems with Applications, 30, 543–552.

Dreman, D., & Berry, M. (1995). Overreaction, Underreaction, and the Low-P/E Effect. Financial Analysts Journal, 51(4), 21–30.

Eilers, D., Dunis, C. L., Mettenheim, H. J., & Breitner, M. H. (2014). Intelligent trading of seasonal effects: A decision support algorithm based on reinforcement learning. Decision Support Systems, 64, 100–108.

Fama, E. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383–417.

Fleanță, S., & Anghel, L. C. (2018). The Romania’s Capital Market Chances of Becoming an Emerging Market. In C. Bratianu et al. (Eds.), Proceedings of Strategica. Challenging the Status Quo in Management and Economics (pp. 180-189), Tritonic.

Jangmin, O., Lee, J., Lee, J. W., & Zhang, B. T. (2006). Adaptive stock trading with dynamic asset allocation using reinforcement learning, Information Sciences, 176(15), 2121-2147.

Kouwenberg, R. (2001). Scenario generation and stochastic programming models for asset liability management. European Journal of Operational Research, 134, 279–292.

Mansini, R., Ogryczak W., & Speranza, M. G. (2003). On lp solvable models for portfolio selection. Informatica, 14, 37–62.

Markowitz, H. M. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91.

Mihalcea, A., & Anghel, L. (2018). Romanian Capital Market: On the Road toward an Emergent Market Status. In Proceedings of Strategica. Challenging the Status Quo in Management and Economics (pp. 168-179), Tritonic.

Moody, J., & Saffell, M. (2001). Learning to trade via direct reinforcement. IEEE Transactions on Neural Networks 12(4), 875-89.

Moody, J., Saffell, M., Liao, Y., & Wu, L. (1998). Reinforcement Learning for Trading Systems and Portfolios: Immediate vs Future Rewards. In A. P. N. Refenes, A. N. Burgess, & J. E. Moody (Eds.), Decision Technologies for Computational Finance. Advances in Computational Management Science (vol. 2), Springer.

Neuneier, R. (1995). Optimal Asset Allocation using Adaptive Dynamic Programming. NIPS.

Neuneier, R. (1997). Enhancing Q-Learning for Optimal Asset Allocation. NIPS.

Zhang, X., Hu, Y., Xie, K., Zhang, W., Su, L., & Liu, M. (2015). An evolutionary trend reversion model for stock trading rule discovery. Knowledge-Based Systems, 79, 27–35.

Zolkepli H. (n.d), Stock Prediction Models.


  • There are currently no refbacks.

Copyright (c) 2021 Management Dynamics in the Knowledge Economy

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© Faculty of Management (SNSPA)

Creative Commons License
This work is licensed under CC BY-NC

The opinions expressed in the papers published are the authors’ own and do not necessarily express the views of the editors of this journal. The authors assume all responsibility for the ideas expressed in the materials published.

ISSN 2392-8042 (online)